Alex Shevchenko
Head of Applied Research at Ramp
About
I'm Alex Shevchenko, currently serving as the Head of Applied Research at Ramp. My journey has taken me from founding my own startup to engineering roles at Microsoft and a rapid growth trajectory at Ramp, where I've moved from a Senior Engineer to leading research in just a few years. I’m deeply passionate about agentic infrastructure and context engineering—essentially building the systems that allow AI to solve real, mundane problems like financial paperwork so people can focus on what actually matters. I thrive in high-velocity shipping cultures and love diving into the technical trade-offs of scaling compute for RL. I’m always looking to jam with other researchers and engineers on infra challenges and deterministic evals. If you're building in the agentic space or looking to simplify complex systems, I'd love to connect.
Networking
What I can offer
- ›Expertise in AI agent infrastructure and context engineering
- ›Insights into high-velocity shipping culture
- ›Technical knowledge of fintech automation and clean ledgers
Looking for
- ›Collaborative research on agent infrastructure
- ›Technical discussions on tradeoffs with peer engineering teams
- ›Opportunities to jam on scaling compute and RL rollouts
Best fit for
Current Interests
Background
Career
Began as a Full Stack Developer and AI Engineer, spent time at Microsoft, then rapidly ascended at Ramp from Software Engineer II to Head of Applied Research in under three years.
Education
Bachelor of Engineering (BE) in Software Engineering from Concordia University; Diplôme Études Collégiales in Sciences Pures et appliquées from Collège Jean-de-Brébeuf.
Achievements
- ›Ascended from SE II to Head of Applied Research at Ramp in ~2.5 years
- ›Developed innovative agents 'Inspect' and 'Steer' at Ramp Labs
- ›Featured panelist at Lux Capital events
- ›Founded Safewatch
Opinions
- True disruption is solving mundane problems like finance paperwork so people can focus on core work.
- Simplicity is essential for any system intended to scale rapidly.
- Building deterministic evaluations is critical for reliable AI research.